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About This Role
About Us
At Netwrix, our mission is to revolutionize data security by placing identity at the core \- providing unparalleled visibility and control. Engineered and supported by over 900 highly talented, motivated employees and hundreds of trusted partners in nearly every geography, Netwrix solutions are relied upon daily by security professionals across more than 13,500 organizations in over 100 countries around the world.
Over the past two decades, Netwrix has expanded its market presence through innovation, organic growth, and strategic acquisitions, and are proud to be backed by renowned private equity firms, TA Associates and Centerbridge Partners. Netwrix maintains a global presence, fostering a remote\-first work environment while encouraging and facilitating frequent face\-to\-face interaction with colleagues, customers, and partners.
Position Overview
The AI Data Engineer designs, builds, and operates enterprise‑grade data and AI platforms using GitOps principles. This role combines data engineering, AI enablement, platform engineering and IT Operations, with a strong emphasis on stability and repeatability.
This role directly supports and enables Netwrix products and internal platforms, ensuring that AI and data capabilities align with Netwrix’s security‑first, governance‑driven mission.
The AI Data Engineer will work with data generated by or integrated into Netwrix solutions such as:
- Netwrix Data Security Platform components, including data access governance, data classification, auditing and identity‑centric security telemetry.
- Platform Governance products (Drata, Salesforce and NetSuite), which generate configuration, change, and audit data requiring structured ingestion and analysis.
- Identity, endpoint, and infrastructure security products (e.g., Active Directory security, endpoint protection, privileged access, configuration management).
- Internal AI Agents and Experience Platforms where data must be securely scoped, versioned and observable across multiple domains/tenants.
Key Responsibilities
GitOps‑Driven Platform \& Pipeline Engineering (GitHub, Azure DevOps, Terraform)
- Design, build and operate data and AI platforms as code, using Git‑based workflows as the source of truth.
- Implement pull‑request‑driven change control, automated testing and CI/CD pipelines.
- Define and maintain Infrastructure‑as‑Code for data and AI systems to ensure consistency, traceability, and rollback capability.
AI \& ML Data Pipeline Engineering (Azure ML Feature Store, Databricks Feature Store)
- Design and maintain scalable ETL/ELT pipelines that support:
- AI/ML model training and retraining
- Feature engineering and feature stores
- Batch and near‑real‑time inference workflows
- Design pipelines backwards from business requirements while accounting for data freshness, latency and reliability.
GenAI \& RAG Enablement (Azure OpenAI and AI Search, internal Netwrix data sources; internal AI agents, secured APIs)
- Support Retrieval‑Augmented Generation (RAG) and internal AI agents by curating, indexing and refreshing select data sources.
- Build and operate pipelines for:
- Embedding generation and lifecycle management
- Vector database ingestion and maintenance
- Context retrieval and prompt‑adjacent data flows
Data Quality, Governance \& Observability (Azure Monitor, ML monitoring, Application Insights)
- Implement proactive monitoring for:
- Data quality and schema integrity
- Pipeline performance and failure modes
- Distribution shifts and data drift impacting AI systems
- Integrate security, privacy and compliance controls directly into pipelines by design. Must partner with both Product and Corporate Security teams.
- Maintain clear, auditable data lineage, ownership and documentation.
MLOps \& Production Readiness (Azure ML Model Registry, Runbooks, operational handoff documentation)
- Partner with Product and Engineering teams to operationalize models by:
- Integrating data pipelines into MLOps workflows
- Supporting model versioning, retraining and rollback strategies
- Enabling observability across data and model performance
- Ensure AI systems/integrations are supportable by IT Operations and Solutions team members; train or provide guidance at a regular cadence.
Cloud \& Platform Engineering (Azure Storage, Azure Kubernetes Service, Azure Container Registry)
- Build and operate Azure‑based data and AI platforms, including storage, compute, orchestration and containerized services.
- Optimize platforms for cost efficiency, performance, reliability and scale in a global, mostly remote work environment.
- Support hybrid or restricted environments where AI systems must meet enterprise or regulatory constraints.
Cross‑Functional Collaboration
- Work closely with:
- + IT Operations \& Platform Engineering *(Intune, Entra ID)*
+ Security \& Governance teams *(Netwrix,* *Drata)*
+ Data Science and AI Engineering *(Azure ML, Azure OpenAI)*
+ Product and business stakeholders *(Salesforce, NetSuite)*
- Translate AI and business requirements into durable, enterprise‑ready architectures.
- Produce clear architecture diagrams, runbooks, and operational documentation.
Required Qualifications
- Bachelor’s Degree in Computer Science, Data Engineering, Engineering, or equivalent practical experience.
- 5 \- 7 years of experience in data engineering, platform engineering, or infrastructure roles.
- Strong proficiency in Python and SQL, with working fluency in JSON, YAML, and shell scripting.
- Experience using Gitbased workflows, Infrastructure as Code, and CI/CD pipelines to build and operate data and AI platforms in production environments.
- Experience operating workloads in Azure and AWS.
- Has performed direct and operational applications of large language models (LLMs) and GenAI platforms (OpenAI, Anthropic Claude, Google Gemini) within enterprise controlled environments.
Our Values
At Netwrix, our values guide every action:
- Next\-Level Customer Focus \-Customers first, always. We listen, protect, and go the extra mile— because their success is our mission.
- Excellence \- We set high standards and take pride in delivering exceptional results. We celebrate wins, seek constant improvement, and address shortcomings professionally.
- Transparent Ownership \- We celebrate our successes, own up to our mistakes, communicate openly, and face challenges head\-on with a genuine commitment to doing the right thing.
- Winning with Clear Thinking \- We value clarity, find straightforward solutions to complex problems, and make swift, effective decisions.
- Relentless Innovation \- We continually seek better ways to serve our customers and stay ahead. We foster creative thinking, and we embrace new approaches.
- Industry\-Leading Expertise \- We take pride in our expertise and continuously seek to learn and share knowledge, striving to be the trusted experts our customers rely on.
- eXceptional Together \- We believe in the power of collaboration and diverse perspectives. By valuing each other’s strengths, we achieve outcomes that surpass individual contributions.
Join us in a culture where integrity, respect, and hard work are foundational. Be part of a team dedicated to making a lasting impact.
Why You’ll Love Working at Netwrix
- Competitive Health Benefits
- Continuous Learning and Development Opportunities
- Team\-Oriented, Collaborative, and Innovative Work Environment
- Regular Company Town Halls to Keep You Informed
- Opportunities for Career Growth and Advancement
We pride ourselves on a culture that truly values employee input across various backgrounds and experiences. We look forward to welcoming new talent who can help us further our mission.
Netwrix Corporation and its wholly owned subsidiaries are Equal Opportunity Employers (EEO) and welcome all applicants for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.
Please let us know if you require any accommodation.
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 26,159 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Netwrix, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $208,300 based on 199 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Netwrix AI Hiring
Netwrix has 1 open AI role right now. They're hiring across Data Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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